【python3】基於隨機森林的氣溫預測

Help on function get_dummies in module pandas.core.reshape.reshape:

get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False, columns=None, sparse=False, drop_first=False, dtype=None) -> 'DataFrame'

     Convert categorical variable into dummy/indicator variables.  

     Parameters

     ----------

     data : array-like, Series, or DataFrame

         Data of which to get dummy indicators.

     prefix : str, list of str, or dict of str, default None

         String to append DataFrame column names.

         Pass a list with length equal to the number of columns

         when calling get_dummies on a DataFrame. Alternatively, `prefix`

         can be a dictionary mapping column names to prefixes.

     prefix_sep : str, default '_'

         If appending prefix, separator/delimiter to use. Or pass a

         list or dictionary as with `prefix`.

     dummy_na : bool, default False

         Add a column to indicate NaNs, if False NaNs are ignored.

     columns : list-like, default None

         Column names in the DataFrame to be encoded.

         If `columns` is None then all the columns with

         `object` or `外匯跟單gendan5.comcategory` dtype will be converted.

     sparse : bool, default False

         Whether the dummy-encoded columns should be backed by

         a :class:`SparseArray` (True) or a regular NumPy array (False).

     drop_first : bool, default False

         Whether to get k-1 dummies out of k categorical levels by removing the

         first level.

     dtype : dtype, default np.uint8

         Data type for new columns. Only a single dtype is allowed.

         .. versionadded:: 0.23.0    

     Returns

     -------

     DataFrame

         Dummy-coded data.    

     See Also

     --------

     Series.str.get_dummies : Convert Series to dummy codes.   

     Examples

     --------

     >>> s = pd.Series(list('abca'))    

     >>> pd.get_dummies(s)

        a  b  c

     0  1  0  0

     1  0  1  0

     2  0  0  1

     3  1  0  0

     >>> s1 = ['a', 'b', np.nan]

     >>> pd.get_dummies(s1)

        a  b

     0  1  0

     1  0  1

     2  0  0    

     >>> pd.get_dummies(s1, dummy_na=True)

        a  b  NaN

     0  1  0    0

     1  0  1    0

     2  0  0    1    

     >>> df = pd.DataFrame({'A': ['a', 'b', 'a'], 'B': ['b', 'a', 'c'],

     ...                    'C': [1, 2, 3]})    

     >>> pd.get_dummies(df, prefix=['col1', 'col2'])

        C  col1_a  col1_b  col2_a  col2_b  col2_c

     0  1       1       0       0       1       0

     1  2       0       1       1       0       0

     2  3       1       0       0       0       1    

     >>> pd.get_dummies(pd.Series(list('abcaa')))

        a  b  c

     0  1  0  0

     1  0  1  0

     2  0  0  1

     3  1  0  0

     4  1  0  0

     >>> pd.get_dummies(pd.Series(list('abcaa')), drop_first=True)

        b  c

     0  0  0

     1  1  0

     2  0  1

     3  0  0

     4  0  0    

     >>> pd.get_dummies(pd.Series(list('abc')), dtype=float)

          a    b    c

     0  1.0  0.0  0.0

     1  0.0  1.0  0.0

     2  0.0  0.0  1.0

None